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Oppiskele Challenge: Solving Task Using AdaBoost Regressor | Commonly Used Boosting Models
Ensemble Learning
course content

Kurssisisältö

Ensemble Learning

Ensemble Learning

1. Basic Principles of Building Ensemble Models
2. Commonly Used Bagging Models
3. Commonly Used Boosting Models
4. Commonly Used Stacking Models

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Challenge: Solving Task Using AdaBoost Regressor

AdaBoost Regressor is an ensemble learning algorithm used for regression tasks.

The principle of work of such a regressor coincides with the principle of work of the AdaBoost Classifier. The only difference is that we use some regression algorithms (linear regression, decision tree regressor, polynomial regression, etc.) as a base model.

The AdaBoostRegressor class in Python provides tools to train the model and make predictions.

Tehtävä

Swipe to start coding

Your task is to create a model to solve the regression task on the diabetes dataset:

  1. Use a simple Linear Regression model as the base model of an ensemble.
  2. Create an AdaBoost Regressor model with the 50 base estimators.
  3. Print MSE to estimate regression quality.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 3. Luku 3
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book
Challenge: Solving Task Using AdaBoost Regressor

AdaBoost Regressor is an ensemble learning algorithm used for regression tasks.

The principle of work of such a regressor coincides with the principle of work of the AdaBoost Classifier. The only difference is that we use some regression algorithms (linear regression, decision tree regressor, polynomial regression, etc.) as a base model.

The AdaBoostRegressor class in Python provides tools to train the model and make predictions.

Tehtävä

Swipe to start coding

Your task is to create a model to solve the regression task on the diabetes dataset:

  1. Use a simple Linear Regression model as the base model of an ensemble.
  2. Create an AdaBoost Regressor model with the 50 base estimators.
  3. Print MSE to estimate regression quality.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

Miten voimme parantaa sitä?

Kiitos palautteestasi!

Osio 3. Luku 3
Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Pahoittelemme, että jotain meni pieleen. Mitä tapahtui?
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